Adjustable Bipedal Gait Generation using Genetic Algorithm Optimized Fourier Series Formulation

This paper presents a method for optimally generating stable bipedal walking gaits, based on a truncated Fourier series formulation with coefficients tuned by genetic algorithm. It also provides a way to adjust the stride-frequency, step-length or walking pattern in real-time. The proposed approach to gait synthesis is not limited by the robot kinematic structure and can be used to satisfy various motion assumptions. It is also easy to generate optimal gaits on terrains of different slopes or on stairs under different motion requirements. Dynamic simulation results show the validity and robustness of the approach. The gaits generated resulted in human-like motions optimized for stability, even walking speed and lower leg-strike velocity of the swing foot

Authors

• Lin Yang
• Chee-Meng Chew
• Aun Neow Poo
• Teresa Zielinska

Keywords

• Genetic algorithms
• Fourier series
• Legged locomotion
• Humans
• Robots
• Stability criteria
• Genetic engineering
• Electronic mail
• Learning
• Trajectory
• Bipedal Gait
• Gait Generation
• Dynamics Simulations
• Walking Speed
• Step Length
• Walking Pattern
• Walking Gait
• Bipedal Walking
• Walking Stability
• Objective Function
• Knee Joint
• Hip Joint
• Joint Angles
• Constant Coefficient
• Penalty Function
• Lower Portion
• Upper Portion
• Coordinate Frame
• Knee Angle
• Human Gait
• Joint Trajectories
• Central Pattern Generator
• Natural Walking
• Human Walking
• Inertial Properties
• Support Phase
• Odd Function
• Singularity Problem
• 5th Order
• Bipedal Locomotion
• Genetic Algorithm
• ZMP stability criterion